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Mid-level, metro location, popular Data Scientist title, and hybrid work increase competition.
Strong ML skill transferability but GCP specialization raises domain bias, so medium sensitivity.
Explicit 5-7 years plus mandatory GCP, Vertex AI, Python, and ML framework requirements increase strictness.
Build, train, deploy, and monitor scalable AI/ML models using Google Cloud Platform services like Vertex AI, BigQuery, Cloud Storage, and Dataflow.
Develop predictive models and data science solutions leveraging Python and ML libraries (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).
Collaborate with cross-functional teams to translate business problems into analytical and AI-driven solutions impacting business outcomes.
5 to 7 years of experience in Data Science, Machine Learning, and AI.
Strong hands-on experience with Google Cloud Platform (GCP) AI/ML services including Vertex AI, BigQuery, Cloud Storage, and Dataflow.
Proficiency in Python and machine learning libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.
Work Experience Required: 5 to 7 years in relevant domains.
Experienced in end-to-end ML lifecycle management including model deployment and MLOps on cloud-native platforms.
Comfortable working with both structured and unstructured datasets to build predictive and analytical solutions.
Able to collaborate effectively with cross-functional teams and translate complex business requirements into AI/ML models.